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Ensuring Data Quality in Collections

This presentation, delivered by Brian Townsend from the Vermont Department of Education, outlines the current and future methods of data collection in Vermont. Highlighting the transition from manual entry to automated systems through the Vermont Automated Data Reporting (VADR) Project, it addresses strategies for data validation, error-checking procedures, and training programs tailored for specific roles. Emphasizing the importance of high-stakes data for funding and accountability, it also discusses resources for ensuring data quality and the significance of timely, accurate reporting for stakeholders.

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Ensuring Data Quality in Collections

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  1. Ensuring Data Quality in Collections Monday, October 29, 2012 Brian Townsend, VT Department of Education

  2. Vermont

  3. Vermont Background • CURRENT: Method of Data Collection • Online: Oracle Forms/Reports Data Collections • Mostly data entry with pre-populated data • Some batch file uploads • Distributed: Microsoft Access • FUTURE: FY12 SLDS Grant (Vermont’s 1st) • Vermont Automated Data Reporting (VADR) Project • Deliverable 1: Statewide Vertical Reporting

  4. VERMONT, CONT. Communication & Training • Application Specific Documentation • On Data Collection website & Inside Oracle applications • Data Collection Trainings • Role-specific based on collection (e.g. Registrar, Business Manager, etc.) • In-person trainings • Online Trainings • Learning Network of Vermont (LNV) • GoTo Suite • Weekly Field Memo • Helpdesk 4

  5. Vermont, cont. Data Validation & Corrections • Application/Database-level validation rules • Error-checking procedures • Backend: Oracle Database procedures • External: SPSS (e.g. frequency checks, auto-fixes, flags to fix manually) • EdFacts reporting: Built in edit checks to ensure EdFacts rules aren’t violated. • Administrator Sign-Off of Data Collection Indicators

  6. VERMONT, CONT. Data Validation & Corrections, cont. • Post-hoc Validation & Correction • Return Error Reports • Disputed Students • Perm Checking (record former last names) • 3-year Revision Window • Can lead to new errors Data Use • High Stakes Data • Membership = Money • Visibility => Data Quality 6

  7. VERMONT, CONT. Data Use, cont. • Town Meeting Reports • Spending & Assessment Results • Public/Legislature/Parents • Compare Assessment Results across schools => Choice Wrap Up • Small State • Demographics make it easier to spot large % change • Anomalies stand out more • SLDS will improve data quality • Timeliness & Availability 7

  8. Contacts & Additional Resources Contact information: Brian Townsend, brian.townsend@state.vt.us Corey Chatis, corey.chatis@sst-slds.org For more information on Data Quality: Statewide Standardized Course Codes: SLDS Best Practices Brief Traveling Through Time: Forum Guide to LDSs, Book IV: Advanced LDS Usage

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